Aurélie Névéol, Xavier Tannier, Pierre Zweigenbaum, Salam Abbara, Rémi Flicoteaux, Youness Khalil, Emmanuel Bacry
We lifted 6 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| xtannier/PAHRAF_cleaning_and_publication | canonical | 4 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| duration_normalization | Ran | xtannier/PAHRAF_cleaning_and_publication/reports_extractor/normalization.py pointer only (licence: NONE) · get_code("e4ebabfa96dd8e07") |
| get_markdown_statistics | Ran | xtannier/PAHRAF_cleaning_and_publication/huggingface/publication.py pointer only (licence: NONE) · get_code("05d0b9f8f4397afd") |
| path_parser | Ran | xtannier/PAHRAF_cleaning_and_publication/reports_extractor/normalization.py pointer only (licence: NONE) · get_code("c8d2288945fabf0d") |
| sex_normalization | Ran | xtannier/PAHRAF_cleaning_and_publication/reports_extractor/normalization.py pointer only (licence: NONE) · get_code("7ae5dc514ae5fa21") |
| match_record_type | Not yet run | xtannier/PAHRAF_cleaning_and_publication/reports_extractor/utils.py pointer only (licence: NONE) · get_code("e42ed5ee896e31c6") |
| match_structured_abstract | Not yet run | xtannier/PAHRAF_cleaning_and_publication/reports_extractor/utils.py pointer only (licence: NONE) · get_code("982495965f5ef0bf") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The development of clinical natural language processing (NLP) systems is severely hampered by the sensitive nature of medical records, which restricts data sharing under stringent privacy regulations, particularly in France and the broader European Union. To address this gap, we introduce PARHAF, a large open-source corpus of clinical documents in French. PARHAF comprises expert-authored clinical reports describing realistic yet entirely fictitious patient cases, making it anonymous and freely shareable by design. The corpus was developed using a structured protocol that combined clinician expertise with epidemiological guidance from the French National Health Data System (SNDS), ensuring broad clinical coverage. A total of 104 medical residents across 18 specialties authored and peer-reviewed the reports following predefined clinical scenarios and document templates. The corpus contains 7,394 clinical reports covering 5,009 patient cases across a wide range of medical and surgical specialties. It includes a general-purpose component designed to approximate real-world hospitalization distributions, and four specialized subsets that support information-extraction use cases in oncology, infectious diseases, and diagnostic coding. Documents are released under a CC-BY open license, with a portion temporarily embargoed to enable future benchmarking under controlled conditions. PARHAF provides a valuable resource for training and evaluating French clinical language models in a fully privacy-preserving setting, and establishes a replicable methodology for building shareable synthetic clinical corpora in other languages and health systems.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2603.20494")
get_code_for_paper("2603.20494")
have("2603.20494")
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